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Description:
We investigate the relationship between (a) official information on COVID-19 infection and death case counts; (b) beliefs about such case counts, at present and in the future; (c) beliefs about average infection chance--in principle, directly calculable from (b); and (d) self-reported health-protective behavior. We elicit (b), (c), and (d) with a daily online survey in the US from March to August 2020 (N ≈ 13,900)
We have three main findings: (1) beliefs elicited as infection case counts are closely related to present and future official case-count information; however (2) beliefs elicited as risk perceptions--i.e., the chance to get infected--are inconsistent with those case-count beliefs, even when mathematically, they should be identical; notably, (3) it is the latter--the risk perceptions--that are significantly better predictors of reported behavior than the former
Together, these findings suggest that researchers and policymakers, who increasingly engage in direct elicitation and communication of numeric measures of uncertainty, may get very different outcomes, depending on which measures they use. We discuss potential implications for public communication of health-risk information